Adaptive analytical behavioral and health assistant system and related method of use

ABSTRACT

This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.

RELATED APPLICATION

This application claims the benefits of priority under 35 U.S.C. §§119and 120 to U.S. Provisional Application No. 61/467,131, filed on Mar.24, 2011, the entirety of which is incorporated herein by reference.

FIELD OF THE DISCLOSURE

Embodiments of the present disclosure relate to systems and methods thataid in a decision-making process. In particular, exemplary embodimentsof the present disclosure relate to aiding a decision-making process forhealth or medical purposes.

BACKGROUND OF THE DISCLOSURE

The management of digital healthcare information is important. Attemptshave been made to provide diagnosis, medical decision support, andhealthcare information management using individual data. These systems,however, lack the ability to empower patients to self-managepost-diagnosis care. For example, these prior attempts fail to use theindividual data to provide advice in order to achieve one or moreclinical goals.

One problem with prior attempts is that they lack the ability to makeanalytical inferences from a patient's behavior and/or clinical data.For example, they lack the ability to adapt an action plan based ondiscovering one or more patterns associated with the patient. Moreover,the prior attempts are not “smart,” because they lack the ability tolearn. The systems and methods described herein solve one or more ofthese problems, or other problems with the prior attempts.

SUMMARY

The present disclosure includes many embodiments. For example, in anembodiment, systems and methods for providing an Adaptive AnalyticalBehavioral and Health Assistant (AABHA) are disclosed.

An aspect of the present disclosure may include a computer-implementedmethod for providing a health assistant system for dynamically creatinga plan. The method may include collecting one or more of patientbehavior information, clinical information, or personal information. Themethod may further include learning one or more patterns that cause anevent based on the collected information and one or more patternrecognition algorithms. The method may further include identifying oneor more interventions to prevent the event from occurring or tofacilitate the event based on the learned patterns. The method mayfurther include preparing the plan based on the collected informationand the identified interventions. The method may further includepresenting the plan to a user or executing the plan.

Various embodiments of the disclosure may include one or more of thefollowing aspects: receiving feedback relating to the plan, and revisingthe plan based on the feedback; the feedback being one or more patientbehaviors that occur after the plan; the revised plan including one ormore additional interventions selected based on the feedback; the one ormore patient behaviors that occur after the plan include a behaviortransition; determining one or more persons to associate with theidentified intervention; automatically revising probabilities from thecollected information; storing the revised probabilities, wherein therevised probabilities are used to determine the plan; and/orautomatically make one or more inferences based on machine learningusing one or more of the clinical information, behavior information, orpersonal information.

A further aspect of the present disclosure may include an informationprocessing device for determining a plan. The device may include aprocessor for processing a set of instructions. The device may alsoinclude a computer-readable storage medium for storing the set ofinstructions, wherein the instructions, when executed by the processor,perform a method. The method may include collecting one or more ofpatient behavior information, clinical information, or personalinformation. The method may further include learning one or morepatterns that cause an event based on the collected information and apattern recognition algorithm. The method may further includeidentifying one or more interventions to prevent the event fromoccurring based on the learned patterns. The method may further includepreparing the plan based on the collected information and the identifiedinterventions. The method may further include presenting the plan to auser.

Various embodiments of the disclosure may include one or more of thefollowing aspects: receiving feedback relating to the plan, and revisingthe plan based on the feedback; the feedback being one or more patientbehaviors that occur after the plan; the revised plan including one ormore additional interventions selected based on the feedback; the one ormore patient behaviors that occur after the plan include a behaviortransition; determining one or more persons to associate with theidentified intervention; automatically revising probabilities from thecollected information; storing the revised probabilities, wherein therevised probabilities are used to determine the plan; and/orautomatically make one or more inferences based on machine learningusing one or more of the clinical information, behavior information, orpersonal information.

Another aspect of the present disclosure includes a non-transitorycomputer-readable medium storing a set of instructions that, whenexecuted by a processor, perform a method for determining an plan. Themethod may include collecting one or more of patient behaviorinformation, clinical information, or personal information. The methodmay further include learning one or more patterns that cause an eventbased on the collected information and a pattern recognition algorithm.The method may further include identifying one or more interventions toprevent the event from occurring based on the learned patterns. Themethod may further include preparing the plan based on the collectedinformation and the identified interventions. The method may furtherinclude presenting the plan to a user.

Various embodiments of the disclosure may include one or more of thefollowing aspects: receiving feedback relating to the plan, and revisingthe plan based on the feedback; the feedback being one or more patientbehaviors that occur after the plan; the revised plan including one ormore additional interventions selected based on the feedback; the one ormore patient behaviors that occur after the plan include a behaviortransition; determining one or more persons to associate with theidentified intervention; automatically revising probabilities from thecollected information; storing the revised probabilities, wherein therevised probabilities are used to determine the plan; and/orautomatically make one or more inferences based on machine learningusing one or more of the clinical information, behavior information, orpersonal information.

Additional objects and advantages of the disclosure will be set forth inpart in the description which follows, and in part will be obvious fromthe description, or may be learned by practice of the disclosure. Theobjects and advantages of the disclosure will be realized and attainedby means of the elements and combinations particularly pointed out inthe appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the disclosure andtogether with the description, serve to explain the principles of thedisclosure.

FIG. 1 depicts a system for providing a health assistant consistent withan embodiment of the current disclosure.

FIG. 2 depicts a flowchart preparing a plan for a user consistent withan embodiment of the current disclosure.

FIG. 3 depicts a system for providing a health assistant consistent withan embodiment of the current disclosure.

DESCRIPTION OF THE EMBODIMENTS

FIG. 1 depicts an embodiment 100 that may use one or more devices, suchas user device 101 and server 104, to aid in a decision making process.The decision making process may include processes that manage apatient's health or medical fitness, manage finances, or manage devicemaintenance and/or repair. For example, these processes may include oneor more of the following: 1) determining whether to change a diet, 2)determining whether to order more medication, 3) determining whether tocontact a doctor, 4) determining whether to change an exercise routine,or 5) determining whether to rest. As a result, a plan for a particularpatient may be developed. These processes may, additionally oralternatively, include one or more of the following: 1) determiningwhether to buy or sell an asset, 2) determining whether to conductmaintenance, 3) determining whether to order or replace a part, or 4)determining whether to take a product out of service.

User device 101 and server 104 may be any type of general computingdevice, such as a mobile computing device, laptop, netbook, server, cellphone, smart phone, personal digital assistant, tablet computer, or anyother device capable of executing one or more instructions. User device101 and server 104 may contain one or more processors, such asprocessors 101-1 and 104-1. Processors 101-1 and 104-1 may be a centralprocessing unit, a microprocessor, a general purpose processor, anapplication specific processor, or any device that executesinstructions. User device 101 and server 104 may also include one ormore memories, such as memory 101-2 and 104-2, that store one or moresoftware modules. Memory 101-2 and 104-2 may be implemented using anycomputer-readable storage medium, such as hard drives, CDs, DVDs, flashmemory, RAM, ROM, etc. Memory 101-2 may store a module 101-3, which maybe executed by processor 101-1. Similarly, memory 104-2 may store amodule 104-3, which may be executed by processor 104-1.

Modules 101-3 and 104-3 may provide one or more services or engines,such as an Adaptive Pattern Service (“APS”), a Statistical Service(“SS”), a Cause-Effect Modeling Service (“CEMS”) or just ModelingService (“MS”), an Intervention Logistics Service (“ILS”), a DeliveryService (“DS”), a Clinical Analysis Service (“CAS”), or any otherservice or engine. A combination of these services or engines mayoperate to provide an Adaptive Analytical Behavioral and HealthAssistant in accordance with embodiments of the present disclosure. Inone embodiment, as a use case, a patient may be a 40 year old Asian malewith diabetes that has inadequate blood glucose control.

User device 101 may further comprise one or more user interfaces (notshown). The user interface may include one or more of a screen, adisplay, a projector, a touch panel, a pointing device, a scrollingdevice, a button, a switch, a motion sensor, an audio sensor, a pressuresensor, a thermal sensor, etc. The user interface may allow one or moreinterfaces to present information to a user, such as a plan orintervention. The user interface may be web based, such as a web page,or a stand-alone application. The user interface may also be configuredto accept information about a user, such as user feedback. The user maymanually enter the information, or it may be entered automatically. Inthe use case, the patient (or the patient's caretaker) may enterinformation such as when medication was taken or what the patientconsumed. For example, user device 101 may include testing equipment(not shown) or an interface for receiving information from testingequipment. Testing equipment may include, for example, a blood glucosemeter or heart rate monitor. The user device 101 may also include one ormore sensors (not shown), such as a camera or microphone, for collectingfeedback from a user. In the use case, the device may include a glucosemeter for reading and automatically reporting the patient's bloodglucose levels.

The system 100 may also include one or more databases, such as database102. While FIG. 1 depicts database 102 as a separate device, database102 may alternatively be located in user device 101 and/or server 104.Database 102 may be implemented using any database technology known toone of ordinary skill in the art, such as relational database technologyor object-oriented database technology.

Database 102 may store data 102-1. Data 102-1 may include 1) a knowledgebase for making inferences, 2) statistical models, and/or 3) patientinformation. Data 102-1, or portions thereof, may be alternatively orsimultaneously stored in server 104 or user device 101.

System 100 also may include a network 105 for facilitatingcommunications between user device 101, server 104, and/or database 102.The network 105 may include wireless or wired links, such as mobiletelephone networks, Wi-Fi, LANs, WANs, Bluetooth, near-fieldcommunication (NFC), etc.

FIG. 2 depicts a process for providing an Adaptive Analytical Behavioraland Health Assistant, in accordance with one embodiment of the presentdisclosure. In step S201, user device 101 or server 104 may obtain datafor one or more patients from database 102. Then, in step S202, userdevice 101 and/or server 104 may use the patient data to identify one ormore patterns. This may be achieved, for example, by an APS.

Next, in step S203, user device 101 or server 104 may apply one or moremathematical models (e.g., the mathematical models discussed below) tothe data. The application of the models may provide insight into thedynamics of a patient's behavior or possible causes of an observedbehavior. The models may be applied or supplied by a GEMS or MS.

User device 101 or server 104 may then use the results of theapplication to develop one or more plans for a patient or user (S204).The development of the plan may be performed, for example, by anIntervention Logistics Service (“ILS”). The user interface may thenpresent the plan to the patient or user (S205) or execute the plan,using, for example, the DS. User device 101 may then receive feedbackfrom the patient or user, testing equipment, or other sensory data(S206) and either process the feedback itself, or send the feedback tothe server 104 for processing by one or more of the services or engines.

While steps S201-S207 are depicted in a particular order, the principlesof the present disclosure are not limited to the order depicted in FIG.2. Additionally, embodiments of the process can include more or lesssteps than those depicted in FIG. 2.

FIG. 3 displays an exemplary embodiment including user device 101,server 104, and a clinical device 301. User device 101 may include apresentation layer 301. Presentation layer 301 may be a web browser,application, messaging interface (e.g., e-mail, instant message, SMS,etc.), etc. The user device 101 may present an action plan,notifications, alerts, reading materials, references, guides, reminders,or suggestions to a user via presentation layer 301. For example, thepresentation layer 301 may present articles that are determined to berelevant to the user, reminders to purchase medications, tutorials ontopics (e.g., a tutorial on carbohydrates), testimonials for others withsymptoms, and/or one or more goals (e.g., a carbohydrate counting goal).The presentation layer 301 may also present information such as atutorial (e.g., a user guide or instructional video) and/or enablecommunications between the healthcare provider and the user. Thecommunications between the healthcare provider and the user may be viaelectronic messaging (e.g., e-mail or SMS), voice, or real-time video.One or more of these items may be presented based on the action plan(e.g., as an intervention) or a determination by one or more of APS 304,SS 305, CEMS 303, ILS 311, DS 302, CAS 312, etc. Presentation layer 301may also be used to receive feedback from a user, as discussed above.This may include, for example, monitoring a patient's actions, a user'sattitude, or a user's belief. Server 104 may include one or more of APS304, SS 305, GEMS 303, ILS 311, DS 302, CAS 312, etc.

Clinical device 301 may include a presentation layer 306 and/or CAS 312.While a single clinical device 301 is shown, the system may includemultiple clinical devices. For example, one or more of a patient'sphysician, therapist, health insurance, or nurse may have a clinicaldevice 301. Presentation layer 306 or CAS 312 may be used to providereminders, notifications, etc. similar to those presented to the userabove. For example, the system may send a notification to a healthcareprovider to open a report on a patient's observed medication patterns.This notification may be sent, for example, in response to adetermination that the patient has a tendency to manage symptoms (e.g.,low blood sugar) with medication alone and/or runs out of medicationearly. A notification may also be sent to the healthcare provider todiscuss one or more topics with a patient. For example, when a clienthas a pattern of managing low blood sugar with medication alone and thenovereats, causing worse symptoms, the system my notify the healthcareprovider to discuss, e.g., the relationships between carbohydrates andthe medication with the patient. Presentation layer 306 or CAS 312 mayalso be used to communicate with a user or patient, as discussed above.

Clinical Device 301 may also be used by a clinical analyst. The clinicalanalyst may use presentation layer 306 or CAS 312 to design metrics,prepare data, set thresholds, design models, define rules, and/or manageknowledge.

DS 302 may include an interface engine 307 and/or intervention executionengine 308. Interface engine 307 may be, for example, a web server thatprovides a webpage or portal to presentation layer 301 and/orpresentation layer 306. Alternatively, interface engine 307 may comprisean application program interface (API) that provides an interface topresentation layer 301, presentation layer 306, and/or CAS 313. Theinterface engine 307 may, for example, provide one or more notificationsto presentation layer 301 and/or presentation layer 306. Interventionexecution engine 308 may provide a means for executing interventionsincluded in an action plan. Intervention execution engine 308 mayfacilitate notifications, tutorials, reminders, and/or communicationwith a healthcare provider, etc. when needed to lower the probabilitythat an event will occur.

In a use case, a patient, Srini, may be 47 years old with diabetes andheart problems. Without assistance, Srini may experience heart pain andrun out of medication and need to go to the emergency room. The systemmay detect that Srini was feeling low (e.g., his blood glucose was low)and he managed this with medication alone. Then, he compensated with toomuch food. This caused the symptoms to worsen, which led to moremedication only management. This led to running out of medication. Whichled to low blood sugar again and more severe symptoms, including heartpain. To prevent this, the system may take the following actions:

-   -   a. In a first week, there may be an article on the importance of        dosage, a reminder may be issued to a patient and/or caregiver        about medication purchase for the month, and/or a notification        to a heath care provider to open report on observed medication        patterns.    -   b. In week two, there may be a notification sent to a patient to        view a tutorial on carbohydrates, a notification to the health        care provider to specifically talk to the patient about the        relationship between carbohydrates and medication, a testimonial        of those with shortness of breath, and a suggestion to the        patient to set a carbohydrate counting goal.

These interventions may limit the probability of having severe symptomsand prevent the patient from needing to go to the hospital.

CEMS 303 may include an intervention planning engine 309 and/or anintervention recommendation engine 310. Intervention planning engine 309and/or intervention recommendation engine 310 may define the criteria ortrigger for notifications, tutorials, reminders, and/or communicationwith a healthcare provider, etc. when needed to lower the probabilitythat an event will occur. Intervention planning engine 309 and/orintervention recommendation engine 310 may use intervention executionengine 308 to facilitate these functions.

CAS 312 may be located in server 104 and/or clinical device 301. CAS 312may include an interface engine 313. Interface engine 313 may be a webserver that presents a webpage for performing clinical analysis topresentation layer 306. Interface engine 313 may also be an API forcommunication with CAS 312 or clinical device 301.

ILS 311 may include intervention execution engine 308, interventionplanning engine 309, and/or intervention recommendation engine 310. Oneor more of these engines may also be part of DS 302 and/or CEMS 303.

The APS 304 may act as an agent for identifying one or more patternsfrom data, and may be implemented using some or all of the features ofthe APS 304 described below. The data may be, for example, behavioral,clinical, and/or personal in nature, and may be stored in one or moredatabases. This can include medical records, meter readings (e.g., bloodglucose meter, heart monitor, etc.), data collected for userinteractions (e.g., emails, Internet usage, blog or social media posts,mood determined by voice pattern or facial recognition, etc.), dataentered by a user or an associated user (e.g., logs of food, exercise,symptoms, lifestyle, likes/dislikes, hobbies etc.), positionalinformation (e.g., GPS information, information inferred based on mobiledevice usage, etc.), information about a patient's attitude and/orbeliefs, perceptions, personality traits, cognitive abilities & skills,emotional intelligence, psychological traits, etc. These one or moredatabases may be part of server 104 or remotely located. The APS 304 mayinclude support for continuous, ordinal, and/or nominal data types. TheAPS 304 may employ pattern recognition, relationship discovery,hierarchical inference, etc. The APS 304 may include knowledge-basedcomponents, training-based components, and/or inference-basedcomponents. In the use case, patterns may include one or more simplepatterns (e.g., the patient has more carbohydrates in the second week ofthe month), patterns of patterns (e.g., the trend of eating morecarbohydrates in the second week continues for two months), differingpatterns (e.g., that patient stops testing in the first week of onemonth and in the third week of another month).

In one embodiment, the APS 304 may obtain a pattern definition in asimple format; predict several time steps in future by using Markovmodels; optimize results based on its predictions; detect transitionbetween patterns; abstract data and extract information to infer higherlevels of knowledge; combine higher and lower levels of information tounderstand about the patient and clinical behaviors; infer frommulti-temporal (different time scales) data and associated information;using variable order Markov models, and/or reduce noise over time byemploying clustering algorithms, such as k-means clustering.

For example, K vectors are randomly chosen and assigned as a clustercenter for applying k-means clustering algorithms. In patternrecognition, the k-means is a method for classifying objects based onthe closest training examples in the feature space. k-NN is a type ofinstance based learning, or lazy learning, where the function is onlyapproximated locally and all computation is differed untilclassification. The Euclidian distance between different patterns inthis vector space may be used to find clusters of patterns. The systemmay assign a new input vector to its closest cluster center and may movethat cluster towards the input vector by a fraction of the Euclideandistance between them.

The APS may use knowledge-based components such as a knowledge-basedrepository (KB). The repository may include clinical information. Forexample, it may include that “eating carbohydrate-rich food causes bloodglucose to increase.” The information may be stored in a variety offormats based on the type of inference employing them. Theknowledge-based repository may act as a repository for some or all ofthe referenced knowledge. For example, it can include reference valuesfor certain consents and variables used for inference. Accordingly, oneor more layers (e.g. a hierarchical pattern processing layer or AdvancedPattern Engine) may subscribe to information from the knowledge-basedrepository. For example, one or more of the services may query theknowledge-based repository when making an inference.

In one embodiment, the knowledge-based repository may aggregate relevantclinical and/or behavioral knowledge from one or more sources. In anembodiment, one or more clinical and/or behavioral experts may manuallyspecify the required knowledge (e.g., using CAS 312). In anotherembodiment, an ontology-based approach may be used. For example, theknowledge-based repository may leverage the semantic web usingtechniques, such as statistical relational learning (SRL).

SRL may expand probabilistic reasoning to complex relational domains,such as the semantic web. The SRL may achieve this using a combinationof representational formalisms (e.g., logic and/or frame based systemswith probabilistic models). For example, the SRL may employ Bayesianlogic or Markov logic. For example, if there are two objects—‘Asianmale’ and ‘smartness’, they may be connected using the relationship‘asian males are smart’. This relationship may be given a weight (e.g.,0.3). This relationship may vary from time to time (populations trendover years/decades). By leveraging the knowledge in the semantic web(e.g., all references and discussions on the web where ‘asian male’ and‘smartness’ are used and associated) the degree of relationship may beinterpreted from the sentiment of such references (e.g., positivesentiment: TRUE; negative sentiment: FALSE). Such sentiments and thevolume of discussions may then be transformed into weights. Accordingly,although the system originally assigned a weight of 0.3, based oninformation from semantic web about Asian males and smartness, may berevised to 0.9.

In an embodiment, Markov logic may be applied to the semantic web usingtwo objects: first-order formulae and their weights. The formulae may beacquired based on the semantics of the semantic web languages. In oneembodiment, the SRL may acquire the weights based on probability valuesspecified in ontologies. In another embodiment, where the ontologiescontain individuals, the individuals can be used to learn weights bygenerative learning. In some embodiments, the SRL may learn the weightsby matching and analyzing a predefined corpora of relevant objectsand/or textual resources. These techniques may be used to not only toobtain first-order waited formulae for clinical parameters, but alsogeneral information. This information may then be used when makinginferences.

For example, if the first order logic is ‘obesity causes diabetes’,there are two objects involved: obesity and diabetes. If data onpatients with obesity and as to whether they were diagnosed withdiabetes or not is available, then the weights for this relationship maybe learnt from the data. This may be extended to non-clinical examplessuch as person's mood, beliefs etc.

The APS 304 may use the temporal dimension of data to learnrepresentations. The APS 304 may include a pattern storage system thatexploits hierarchy and analytical abilities using a hierarchical networkof nodes. The nodes may operate on the input patterns one at a time. Forevery input pattern, the node may provide one of three operations: 1.Storing patterns, 2. Learning transition probabilities, and 3. Contextspecific grouping.

A node may have a memory that stores patterns within the field of view.This memory may permanently store patterns and give each pattern adistinct label (e.g. a pattern number). Patterns that occur in the inputfield of view of the node may be compared with patterns that are alreadystored in the memory. If an identical pattern is not in the memory, thenthe input pattern may be added to the memory and given a distinctpattern number. The pattern number may be arbitrarily assigned and maynot reflect any properties of the pattern. In one embodiment, thepattern number may be encoded with one or more properties of thepattern.

In one embodiment, patterns may be stored in a node as rows of anmatrix. In such an embodiment, C may represent a pattern memory matrix.In the pattern memory matrix, each row of C may be a different pattern.These different patterns may be referred to as C-1, C-2, etc., dependingon the row in which the pattern is stored.

The nodes may construct and maintain a Markov graph. The Markov graphmay include vertices that correspond to the store patterns. Each vertexmay include a label of the pattern that it represents. As new patternsare added to the memory contents, the system may add new vertices to theMarkov graph. The system may also create a link between to vertices torepresent the number of transition events between the patternscorresponding to the vertices. For example, when an input pattern i isfollowed by another input pattern j for the first time, a link may beintroduced between the vertices i and j and the number of transitionevents on that link may be set to 1. System may then increment thenumber of transition counts on the link from i and j whenever a patternfrom i to pattern j is observed. The system may normalize the Markovgraph such that the links estimate the probability of a transaction.Normalization may be achieved by dividing the number of transitionevents on the outgoing links of each vertex by the total number oftransition events from the vertex. This may be done for all vertices toobtain a normalized Markov graph. When normalization is completed, thesum of the transition probabilities for each node should add to 1. Thesystem may update the Markov graph continuously to reflect newprobability estimates.

The system may also perform context-specific grouping. To achieve this,the system may partition a set of vertices of the Markov graph into aset of temporal groups. Each temporal group may be a subset of that setof vertices of the Markov graph. The partitioning may be performed suchthat the vertices of the same temporal group are highly likely to followone another.

The node may use Hierarchical Clustering (HC) to for the temporalgroups. The HC algorithm may take a set of pattern labels and theirpair-wise similarity measurements as inputs to produce clusters ofpattern labels. The system may cluster the pattern labels such thatpatterns in the same cluster are similar to each other.

In one embodiment, the probability of a transition between two patternsmay be used as the similarity between those patterns for the HCalgorithm. The similarity metric may be used to cluster medical patternsthat are likely to follow one another into the same cluster. The HCalgorithm may be configured such that patterns that are unlikely tofollow each other fall into different clusters. A cluster of a set ofpatterns that are likely to follow each other in time may be referred toas a temporal group. The HC algorithm may start with all store patternsand separate clusters and then recursively merge clusters with thegreatest similarity. This may be used to obtain a treelike structure(e.g. a dendrogram) with a single cluster (which may contain allpatterns) at the top of the tree and the individual patterns at thebottom (e.g. each pattern in its own cluster). The system may achievethe desired clustering for temporal grouping (e.g. somewhere between thebottom and a top of the dendrogram) by defining a suitable criteria. Forexample, one criterion could be to cut the tree at a level where thesize of the largest cluster does not exceed a particular value. The nodemay have a design perimeter that sets the maximum number of clusters ortemporal groups of the node. The desired temporal groups may be achievedby selecting a level of the dendrogram that gives the number of temporalgroups closest to and less than the configured maximum number oftemporal groups. These temporal groups may be updated as the Markovtransition probabilities are updated. These steps may be performedperiodically during the learning process. The learning process may bestopped once the temporal groups have sufficiently stabilized.

Once a node has completed its learning process, it may be used forsensing and/or inference. The characteristics of the input to the nodein sensing may be identical to those used during learning. For example,objects may move under the field of view of the node and the node maysee portions of those objects. The resulting patterns may be used asinputs to the node,

A node used for sensing and/or inference may produce an output for everyinput pattern. A node may also use a sequence of patents to produce anoutput. In one embodiment, it can be assumed that the outputs areproduced based on instantaneous inputs. Under this assumption, theMarkov graph may not be used during the sensing phase. For example, itmay be discarded once the temporal groups within the node are completed.

For every input pattern, the node may produce an output factor thatindicates the degree of membership of the input pattern and each of itstemporal groups. However, the current input pattern may not perfectlymatch any of the patterns stored in memory. Accordingly, in oneembodiment, the closeness of the input pattern to every pattern storedin memory will be determined. For example, let d_(i) be the distance ofthe i^(th) stored pattern from the input pattern. The larger thisdistance is, the smaller the match between the input pattern and thestored pattern becomes. Assuming that the probability that an inputpattern matches a stored pattern falls off as a Gaussian function of theEuclidean distance, the probability that the input pattern matches thei^(th) stored pattern can be calculated as being proportional toe^(−d2i/α), where α is a parameter of the node. Calculating this forevery stored pattern may give the closeness of the current input patternto all the vertices of the Markov graph.

Degree of membership of the input pattern in each temporal group may bedetermined by the maximum of its closeness to each of the verticeswithin the temporal group. This results in a length equal to the numberof temporal groups, with each component of the factor indicating thedegree of membership of the input pattern in the corresponding temporalgroup. This factor may then be used normalize the sum to unity. Thesenormalized memberships may be used as estimates of probability ofmembership in each temporal group. This normalized degree of membershipmay also be used as an output of the node. The output may be a histogramgiving estimates of probability of membership of the current inputpattern and each of the temporal groups of the node.

As data is fed into the APS 304, the transition probabilities for eachpattern and pattern-of-patterns (POP™) may be updated based on theMarkov graph. This may be achieved by updating the constructedtransition probability matrix. This may be done for each pattern inevery category of patterns. Those with higher probabilities may bechosen and placed in a separate column in the database called aprediction list.

Logical relationships among the patterns may be manually defined basedon the clinical relevance. This relationship is specified as first-orderlogic predicates along with probabilities. These probabilities may becalled beliefs. In one embodiment, a Bayesian Belief Network (BBN) maybe used to make predictions using these beliefs. The BBN may be used toobtain the probability of each occurrence. These logical relationshipsmay also be based on predicates stored the knowledge base.

The APS 304 may also perform optimization for the predictions. In oneembodiment, this may be accomplished by comparing the predictedprobability for a relationship with its actual occurrence. Then, thedifference between the two may be calculated. This may be done for poccurrences of the logic and fed into a K-means clustering algorithm toplot the Euclidean distance between the points. A centroid may beobtained by the algorithm, forming the optimal increment to thedifference. This increment may then be added to the (p+1)^(th)occurrence. Then, the process may be repeated. This may be done untilthe APS 304 predicts logical relationships up to a specified accuracythreshold. Then, the results may be considered optimal.

When a node is at the first level of the hierarchy, its input may comedirectly from the data source, or after some preprocessing. The input toa node at a higher-level may be the concatenation of the outputs of thenodes that are directly connected to it from a lower level. Patterns inhigher-level nodes may represent particular coincidences of their groupsof children. This input may be obtained as a probability distributionfunction (PDF). From this PDF, the probability that a particular groupis active may be calculated as the probability of the pattern that hasthe maximum likelihood among all the patterns belonging to that group.

The APS 203 may also be adaptive. In one embodiment, every level has acapability to obtain feedback information from higher levels. Thisfeedback may inform about certain characteristics of informationtransmitted bottom-up through the network. Such a closed loop may beused to optimize each level's accuracy of inference as well as transmitmore relevant information from the next instance.

The APS 203 may learn and correct its operational efficiency over time.This process is known as the maturity process of the system. Thematurity process may include one or more of the following flow of steps:

-   -   a. Tracking patterns of input data and identifying predefined        patterns (e.g. if the same pattern was observed several times        earlier, the pattern would have already taken certain paths in        the hierarchical node structure).    -   b. Scanning the possible data, other patterns, and POP™        (collectively called Input Sets (IS)) required for those paths.        It also may check for any feedback that has come from higher        levels of hierarchy. This feedback may be either positive or        negative (e.g., the relevance of the information transmitted to        the inferences at higher levels). Accordingly, the system may        decide whether to send this pattern higher up the levels or not,        and if so whether it should it send through a different path.    -   c. Checking for frequently required ISs and pick the top ‘F’        percentile of them.    -   d. Ensuring it keeps this data ready.

In one embodiment, information used at every node may act as agentsreporting on the status of a hierarchical network. These agents arereferred to as Information Entities (In En). In En may provide insightabout the respective inference operation, the input, and the resultwhich collectively is called knowledge.

This knowledge may be different from the KB. For example, the abovedescribed knowledge may include the dynamic creation of insights by thesystem based on its inference, whereas the KB may act as a reference forinference and/or analysis operations. The latter being an input toinference while the former is a product of inference. When thisknowledge is subscribed to by a consumer (e.g. administering system oranother node in a different layer) it is called “Knowledge-as-a-Service(KaaS) which may serve one or more of the following purposes:

-   -   a. When knowledge is subscribed to, it may help in identifying        those In En that are relatively more active than the rest.        Hence, the frequency of subscription may be tracked and the most        active In En may be prioritized accordingly. This may be useful        for situations where communication bandwidth is limited and not        all input can be provided to all nodes at once. In those cases,        the input corresponding to the “most common In En” may be sent        first, thereby satisfying the majority of the requirements to        the nodes. It is to be noted that In En may be state bound and        static (e.g., with time, new knowledge is derived from old one).        But the parent-child relationship between them may be        established and used as a reference. The new knowledge entity        (NeoEn) may be used in another inference in another node. This        NeoEn may also report to a subscriber. In one embodiment, the        subscriber need not necessarily call the respective In En or        NeoEn. Rather, the subscriber may define certain conditions        alone. Any InEn/NeoEn that satisfies these conditions may        automatically provide information. Each InEn and NeoEn may also        include metadata where a set of rules are defined which makes it        eligible to be considered for respective situations. These        situations may be defined by a service provider in the        conditions.    -   b. By reporting information, InEn/NeoEn may provide feedback on        the hierarchical network. This process may aid in detection of        malfunctions or errors in nodes and/or create a log registry for        the activities of nodes.    -   c. Time-based reporting may track trends of knowledge maturity        as well as overall functioning of the hierarchical network.

The APS 304 may further include a Smart Data Runner (SDR). The SDR maychoose the most relevant IS to subscribe to (e.g. for use in a giveninference in a given node for a given context (time & state)). The SDRmay assist an analyst to define IS to be used for each operation in agiven hierarchical layer. The SDR may automate certain aspects of thetasks done by the analyst. This process may include one or more of thefollowing:

-   -   a. For every event to be defined, a list of data variables        and/or their patterns and POP™ may be provided to the analyst by        the SDR.    -   b. The analyst may choose a particular set of data, patterns,        and/or POP™ (e.g input) and may assign one or more weights.        These weights may signify the priority of each input within a        given class of inference.    -   c. Inferences can be organized into various classes such as:        -   i. Time-based and frequency-based;        -   ii. Short-term and long-term; and/or        -   iii. Single inference and multiple related inferences;    -   d. Various combinations of input may be used for the given        inference. In one embodiment, a comparison analysis is done and        coefficients of dependencies are found.    -   e. Based on a differential operator, independent relationships        (i.e. each variable with the resulting variable) may also be        found.    -   f. These coefficients and relationships for every combination        may be provided to the analyst. All combinations may then be        ordered by decreasing order of probability calculated for the        resulting variable. This may ensure that the best results alone        are on the top and chosen, thereby considering the associated        coefficients & relationships to be optimal.    -   g. The analyst then may change weights or remove any variable(s)        (e.g., using CES 312 or presentation layer 306). The analyst may        also provide a rationale for the change (e.g. by selecting a        reason from a list of them.)    -   h. The SDR may perform K-means clustering on the difference        between modified weights and initial weights to find the        centroid and hence the optimum value of delta. It is to be noted        that this may be done on ‘n’ events in a given class of        inference. In one embodiment, this delta is specific to a given        cause(s) as specified by the analyst.    -   i. When the next event data (e.g. training data, not an actual        event) is provided, if the given causes match, then the SDR may        add the optimum delta to specified weight and may provide new        weights to the analyst.    -   j. This procedure may be repeated until delta becomes        infinitesimally small (e.g. an accuracy of 95%). For example,        the SDR may accurately predict the weights that would be        manually entered by the analyst.    -   k. When higher levels of inference are defined, the variables        with high weights in each class may be taken and compared with        each other (i.e., interclass).

Accordingly, the SDR may help identify the most relevant information tobe considered for a given inference. The SDR may be an offline systemused by an analyst. Because the specified version may operate in a humansupervised mode, SDR may not dynamically make decisions on choosingrelevant information on its own when overall system runs online on theactual reported data.

Once the analyst designs all inferences, the respective information tobe used as input may specified in the following fashion.

-   -   a. Every inference may have a space-based and/or time-based        context, specified as values for certain variables called        Context Variables (CV). CV could be Boolean and/or numeric and        may be placed outside the transactional database.    -   b. Every IS may include metadata by which certain conditions are        specified that relate to each possible context within the scope        of overall application.    -   c. Whenever a context is specified, all variables that have        conditions in metadata related to that context may process their        conditions. If found eligible, they may be communicated to the        application which then uses them for the given inference.

This process may extend the analyst-based analysis by adding time-basedconfiguration capabilities.

In the SDR, various dependencies between IS may be specified.Accordingly, the system may use this information to find combinationswithin IS and their respective probabilities. These combinations may beautomated and used to discover new patterns and relationships betweenthem. This process is known as ‘Relationship Discovery’. Since the SDRmay operate on both live and example data it can operate in online andoffline modes.

When it is operated in an online mode, it may serve as a relationshipdiscovery tool. Similar to methodology followed in the SDR, the systemmay try to mimic the accuracy of the analyst in the process of definingnew rules from discovered relationships.

The APS 304 may use one or more communication protocols. For example,APS 304 may outsource the statistical operations involved at variouslevels, such as K-means clustering, regression analysis, etc., to the SS305. Both SS 305 and APS 304 may call the each other on demand. When theSS 305 is the service provider, the APS 304 may subscribe to statisticalresults. But when APS 304 is the service provider, the subscription maybe probability values, data, or patterns.

The APS 304 may also communicate with the CEMS 312. The APS 304 mayprovide information on patterns and POP™. One purpose of APS 304 may beto provide KaaS to CEMS 312. But, the GEMS 312 may access thisinformation as coefficients for behavior and/or clinical models.Accordingly, the pattern comparison characteristics at all levels may betransformed into relative measures. These measures form the modelcoefficients. They may be sent from APS 304 to GEMS 312. In return, oncethe cause-effect analysis is done, the result may be communicated asfeedback to APS 304. This way, the APS 304 can improve its accuracy ofthe information it delivers the next instance.

APS 304 may also communicate the temporal information of patterns anddata to the ILS 311. This communication may be used in determining thesequence of interventions during intervention planning.

The SS 305 may include statistical techniques, methods, and/oralgorithms to be used by one or more of the other services, and may beimplemented using some or all of the features or the type of statisticalanalysis of the SS 305 as described herein.

The CEMS 312 may include various mathematical models related to apatient which may be specific to one or more behaviors and/ordeterminants, and may be implemented using some or all of the featuresof the CEMS 312 as described below.

The CEMS 312 may provide insight about the dynamics of a patient'sbehavior and an analysis of causes for an observed behavior. Some of thefeatures of the CEMS 312 may include dynamically updating coefficientsfor parameters in any given model, based on information from the APS304, performing cause-effect analysis online, attaching a weight foreach cause and infer one or more causes to be reasons for an observedbehavior, providing feedback on the effectiveness of each intervention,including user experience, and/or providing insights about necessity fornewer types of interventions.

CEMS 312 may be used for identifying behavior and clinical causes forevents and their impact(s) on the desired outcome. An event is generallyan observed abnormality in certain conditions. This can be bothprolonged and discrete in nature. By prolonged, it means that the eventis happening for a considerable length. Discrete events are singleoccurrences. The CEMS 312 may include several mathematical and logicalmodels which essentially provide relationships among various factors ofcause and effect for a given context. Then, the CEMS 313 may map them to‘actions’ taken by the patient/end-user as well as those taken byassociated users. Their actions directly/indirectly can affect theoutcome for the end-user. Hence the Associated User (AU) and theirreported data may also be included along with patient data, for thesemodels.

Behavior models are classified into four categories as follows:

-   -   a. Outcome-based;    -   b. Behavior-based;    -   c. Determinant-based; and    -   d. Intervention-based.

One or more of the following rules of thumb may be applied duringbehavioral modeling:

-   -   One or more interventions affect determinants;    -   One or more determinants affect behavior; and    -   One or more behaviors affect outcome.

A behavior is defined to be a characteristic of an individual or a grouptowards certain aspects of their life such as health, socialinteractions, etc. These characteristics are displayed as their attitudetowards such aspects. In analytical terms, a behavior can be consideredsimilar to a habit. Hence, a behavior may be observed POP™ for a givendata from a user. An example of a behavior is dietary habits.

Determinants may include causal factors for behaviors. They either causesomeone to exhibit the same behavior or cause behavior change. Certaindeterminants are quantitative but most are qualitative. Examples includeone's perception about a food, their beliefs, their confidence levels,etc.

Interventions are actions that affect determinants. Indirectly theyinfluence behaviors and hence outcomes. System may get both primary andsecondary sources of data. Primary sources may be directly reported bythe end-user and AU. Secondary data may be collected from sensors suchas their mobile phones, cameras, microphone, as well as those collectedfrom general sources such as the semantic web.

These data sources may inform the system about the respectiveinterventions. For example, to influence a determinant calledforgetfulness which relates to a behavior called medication, the systemsends a reminder at an appropriate time, as the intervention. Then,feedback is obtained whether the user took the medication or not. Thishelps the system in confirming if the intervention was effective.

The system may track a user's interactions and request feedback abouttheir experience through assessments. The system may use thisinformation as part of behavioral modeling to determine if the userinterface and the content delivery mechanism have a significant effecton behavior change with the user. The system may use this information tooptimize its user interface to make it more personalized over time tobest suit the users, as well as to best suit the desired outcome.

For example, if the user provides feedback that they dislike textualdisplay of information and also that they feel the quantity of contentdelivered is excessive, that feedback may be used as an input tooptimize and/or choose the content and delivery method desirable by theuser. In this case, it can be a simple visualization of information(instead of textual) and restricting it to 3 contents per day (e.g.,instead of 10).

Every term in a given behavior model may be assigned a coefficient.Coefficient may signify the relationship it has with other terms for agiven result. A simple example of a behavioral model is shown below.

(Determinants)β1X1+β2X2+β3X3+β4X4=Y(Behavior)

(Interventions)φZ1+φ2Z2+φ3Z3=Xj(Determinant)

For example, in the above liner model, β1, β2 and β3 are thecoefficients for interventions φ1, φ2 and φ3. For example, φ1 may bemessages, φ2 may be goals, and φ3 may be learning libraries.

These interventions are mapped to a determinant Xi. Hence the influenceof these interventions on an observed determinant change is found. Thecoefficients are used as a means to identify respective causes and theirdegrees of influence.

The coefficients may be constructed based on information about observedpatterns from the APS 304. Event(s) information along with relevantpatterns, variables, and their relationship information may be sent aspart of this subscription of service. This may then be used in awell-defined Behavioral Mathematics framework to exactly identify causesfor the observed effect.

Models in CEMS 312 also may accommodate data obtained directly from theend-user, such as assessments, surveys, etc. This enables users to sharetheir views on interventions, their effectiveness, possible causes, etc.The system's understanding of the same aspects is obtained by way ofanalysis and service by the APS 304.

Both system-perceived and end user-perceived measures of behavioralfactors may be used in a process called Perception Scoring (PS). In thisprocess, hybrid scores may be designed to accommodate both abovementioned aspects of behavioral factors. Belief is the measure ofconfidence the system has, when communicating or inferring oninformation. Initially higher beliefs may be set for user-perceivedmeasures.

But over time, as the system finds increasing patterns as well asobtains feedback in APS 304, the GEMS 312 may evaluate the effectivenessof intervention(s). If the system triggers an intervention based onuser-perceived measures and it doesn't have significant effect on thebehavior change, the system may then start reducing its belief foruser-perceived measures and instead will increase its belief forsystem-perceived ones. In other words, the system starts believing lessin the user and starts believing more in itself. Eventually this reachesa stage where system can understand end-users and their behavioralhealth better than end-users themselves. When perception scoring is donefor each intervention, it may result in a score called InterventionEffectiveness Score (IES).

Perception scoring may be done for both end-users as well as AU. Suchscores may be included as part of behavior models during cause-effectanalysis.

Causes may be mapped with interventions, determinants, and behaviorrespectively in order of the relevance. Mapping causes withinterventions helps in back-tracking the respective AU for that cause.In simple terms, it may help in identifying whose actions have had apronounced effect on the end-user's outcome, by how much and using whichintervention. This is very useful in identifying AUs who are veryeffective with specific interventions as welt as during certain eventcontext. Accordingly, they may be provided a score called AssociatedUser Influence Score (AU IS). This encompasses information for a givenend-user, considering all interventions and possible contexts relevantto the AU.

The CEMS 312 may also use one or more communication protocols. Forexample, certain behavior models may require statistical operations suchas regression analysis, clustering, etc. Those tasks may be outsourcedto the SS 305 on demand. The statistical result may then be communicatedback to GEMS 312 by suitable conversion of the results in aCEMS-readable format. The CEMS 312 may also provide sets of causes,their coefficients, IES, and/or AUIS to the ILS 311.

The ILS 311 may construct one or plans including one or moreinterventions based on analysis performed by the GEMS 312, and may beimplemented using some or all of the features of the ILS 311 asdescribed below.

Some of the features of the ILS 311 may include evaluating eligibilityof an intervention for a given scenario, evaluating eligibility of twoor more interventions based on combinatorial effect, prioritizinginterventions to be applied, based on occurrence of patterns (from APS304), and/or submitting an intervention plan to DS 302 in a formatreadily usable for execution.

In one embodiment, the ILS 311 may suggest to the AU a few tasks whilethe system directly implements other tasks to the end user. Hence, theend user gets the impact of these tasks, both from AUs as well as fromthe system. The AU may provide information about the progress towardsdesired outcome. This desired outcome may be twofold in nature—one thatis global and other that is local. Global refers to holistic outcomesthat cannot be achieved directly. These outcomes need few other outcomesto be achieved as a prerequisite. Local refers to those outcomes thatare independent and can be achieved directly. Hence, a global outcomemay require two or more local outcomes to be achieved. An example is tohave a good quality of life, which is a global outcome in this case, oneis required to have stable health, stable personal finances, etc., whichare local outcomes.

This service may rely on the cause-effect analysis by CEMS, for itsplanning operations. A plan consists of interventions and a respectiveimplementation schedule. Every plan may have several versions based onthe users involved in it. For example, the system may have a separateversion for the physician as compared to a patient. They will in turn dothe task and report back to the system. This can be done either directlyor the system may indirectly find it based on whether a desired outcomewith the end user was observed or not.

The methodology employed by SDR and ILS 311 may be predefined by ananalyst. For every cause, which can be an intervention(s),determinant(s), behavior(s) or combinations of the same, the analyst mayspecify one or more remedial actions using CEMS 312 or presentationlayer 306. This may be specified from the causal perspective and not thecontextual perspective.

This approach uses the idea of situation-aware metadata descriptionframework meaning that context specific information for all possiblecontexts specified in the meta data during system design. This processmay be done for every cause, thereby specifying what remedial actionsare more appropriate for it and for what context.

Accordingly, a set of remedial actions may be confirmed by consideringIES as well. AUIS may be used in determining which users shouldparticipate in the intervention plan and how. For example, if aphysician had a high AUIS score for improving confidence of a patient,the intervention plan may consider that fact and include one of theinterventions to be motivational interviewing of the patient by thatphysician, provided the cause-effect analysis confirms it with a closelyassociated cause.

The ILS 311 may communicate with the DS 302 for status update on thecurrent set of interventions being sent to all users. Accordingly, theILS may optimize its plan to ensure that it reflects a smooth transitionbetween plans (current versus new) and hence interventions to the enduser. The rules for such optimizations may be manually specified by theanalyst during the design.

The DS 302 may be responsible for executing the plan provided by the ILS311, and may be implemented using some or all of the features of the DS302 as described below. Some of the features of the DS 302 may includeselecting appropriate presentation mode (e.g. web or mobile) as well asdevice (e.g. phone or tablet) based on the intervention specified,mapping rules to each intervention based on plan from ILS 311, operatingboth proactively (based on predefined plan) and reactively(feedback-based inference engine), obtaining feedback about the end-userfrom system usage and self-reporting, facilitating data to the APS 304and hence made the overall end-to-end CDSS a reflexive feedback system,and/or calling the APS 304 on-demand. In the use case, the patient maybe presented with articles related to the consumption of carbohydrates,given one or more carbohydrate-counting goals, and/or receive one ormore notifications to perform a task, such as instructions to pick upmedication or exercise.

The ILS 311 may send a new plan to DS 302 based on subscription by DS302. This may be initiated by DS 302 when it nears the completion of thecurrent plan. The DS 302 also has the flexibility of a ‘push’ of plansby ILS 311. This may be done when there is a need for change of planssignificantly earlier than the end of current one. The new plan may beentirely different from the current one or it may be additional tasks inthe current plan. This may be confirmed based on synchronization betweenILS 311 and DS 302 during creation of the new plan.

The reason for using a plan is explained as follows. The DS 302 may beresponsible for delivering appropriate tasks to all users by suitablepresentation channels. In the case of a weak communication signal or aloss of network connectivity, the plans may still be executed by usersdue to criticality to end-users health. Accordingly, the plans may besent to the DS 302 to complete the plan and accomplish required outcomeseven in such scenarios.

On the other hand DS 302 can take advantage of a strong network coverageand higher bandwidth to customize the plan to deliver richer and moredynamic content, quicker analysis and superior real-time userexperience.

The DS 302 may transform a plan into versions based on the presentationchannel (web, mobile, standalone, executable program, etc.) and theircapabilities. The DS 302 may employ an inference engine 307 whichprovides real-time support to users based on the specified plan.Inference engine 307 may be device resident and may obtain user-reportedas well as device-reported data. It then facilitates this data tovarious services through the DS 302. Every task in a plan may beoptimized to be compatible and used in an inference engine. This enginemay make certain decisions based on predefined rules. Each plan mayreflect changes to these rules to accommodate the revised outcome to beachieved. The rules may be mapped to content. Each piece of content maybe delivered to the user by a presentation channel.

Various presentation channels are:

-   -   a. Standalone devices (computers)    -   b. Portable devices (mobile phones, handheld devices,        intelligent systems in automobiles—cars, flights etc.)

Various presentation modes are:

-   -   a. Web portal-based    -   b. Application-based        -   i. Standalone executable application        -   ii. Server-based application

There may be a communication component within every presentation channelthat accepts its version of the plan through a secured communicationprotocol. There may also be an execution component within everypresentation layer that will take care of starting the new plan at acalculated period before the completion of the current plan. Thisensures that the transition between plans is smooth enough for the usersto not experience a drastic change in interventions. Also, the executioncomponent may also suppress certain flows of delivering content in theinference engine based on the new plan. This ensures that the users arenot sent contents from two plans which may look confusing. Consideringthe context, either the new plan or the current plan may be preferredover the other. This component also may manage between both planssimultaneously, if necessary.

Every presentation channel may be associated with a presentation device.The capabilities and various sensory data input options of the devicemay be used by inference engine. The device-specific version of the planis meant to address this issue. In certain cases, such as mobile phones,handheld devices, etc., where camera and microphone is available, aspecific pattern recognition engine, called the Sensory PatternRecognition Engine (SPRE), is used. The purpose of this engine is totake specific samples of pictorial, video and auditory data, usingcamera and microphone, and then compare it with predefined categories ofemotions. Hence, it may perform a pattern classification operation. Theengine may be pre-trained with example data in these formats for thoserespective categories. The pattern recognition mechanism may be similarto the one performed by APS 304. But in this engine, the focus may be ononly sensory inputs and basic classification. Thus, it may not have anyhierarchical layers of computation making it much simpler and morespecific. After classification, it may compare the classificationbetween the three sensory data sources and confirm the emotional stateof the end user using suitable algorithms. This feature becomes avaluable source of information that may be used by different servicesfor making psychological and behavioral inferences.

Accordingly, the DS 302 may send a variety of data and information toAPS 304 and other services, as feedback, for these services tounderstand about the users. This understanding may affect their next setof plans which in turn becomes an infinite cyclic system where systemaffects the users while getting affected by them at the same time. Sucha system is called a reflexive-feedback enabled system. The system mayuser both positive and negative reflexive-feedback, though the negativefeedback aspect may predominantly be used for identifying gaps that thesystem needs to address.

Though DS 302 can subscribe to new plans through the ILS 311, it is freeto communicate to other services by bypassing ILS 311. The DS 302 cansubscribe to the APS 304 on demand and request for information such aspatterns, their correlations, etc. which can be directly reported tousers without requiring any analysis through CEMS 312. The APS 304 mayprovide the requested data and the DS 302 may take care of suitablereporting version based on the users and presentation channel

The CAS 303 may provide information, such as one or more newlyidentified patterns, to an analyst (e.g., clinical analyst or doctor).In the use case, the doctor may be presented with one or morenotifications to address the relationship between carbohydrates and themedication that the patient is taking.

The embodiments described herein, and in the attached documentation, areexemplary only, and it will be apparent to those skilled in the art thatvarious modifications and variations can be made in the disclosedsystems and processes without departing from the scope of the invention.Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only.

1-20. (canceled)
 21. A computer-implemented method for dynamicallyrevising a patient care plan comprising: electronically receivingpatient feedback relating to a patient care plan; decoding patientspecific data and learning one or more patterns that cause an eventbased on the patient specific data and one or more pattern recognitionalgorithms; determining one or more patient beneficial actions based onthe learned patterns; and automatically preparing and presenting arevised patient care plan based on the patient specific data, theidentified one or more patient beneficial actions, and the patientfeedback.
 22. The computer-implemented method of claim 21, wherein thepatient specific data comprises data received from a patient body samplesensor.
 23. The computer-implemented method of claim 21, wherein thepatient specific data is a blood glucose level.
 24. Thecomputer-implemented method of claim 21, wherein the patient specificdata comprises patient interaction data.
 25. The computer-implementedmethod of claim 21, wherein the revised patient care plan is presentedto a user on multiple electronic devices.
 26. The computer-implementedmethod of claim 21, wherein the revised patient care plan compriseselectronic content.
 27. The computer-implemented method of claim 21,further comprising determining the speed of the patient's electronicdevice and presenting the revised patient care plan based on the speedof the patient's electronic device.
 28. The computer-implemented methodof claim 27, wherein electronic data type presented in the revisedpatient care plan is determined based on a processor speed of theelectronic device and a speed of an electronic network to which theelectronic device connects.
 29. The computer-implemented method of claim21, wherein the patient specific data is received from a patient'selectronic device and comprises patient environmental data.
 30. Thecomputer-implemented method of claim 29, wherein the patientenvironmental data is received from optical and audio components of theelectronic device.
 31. A computer-implemented method for dynamicallyrevising a patient care plan comprising: electronically receivingpatient feedback relating to the patient care plan; decoding patientspecific data and learning one or more patterns that cause an eventbased on the patient specific data; transmitting an alert related to anon-beneficial patient action; determining one or more patientbeneficial actions based on the learned patterns; and; automaticallypreparing a revised patient care plan based on the patient specificdata, the identified one or more patient beneficial actions, and thepatient feedback.
 32. The computer-implemented method of claim 31,wherein the patient specific data comprises data received from a patientbody sample sensor.
 33. The computer-implemented method of claim 31,wherein the patient specific data is a blood glucose level.
 34. Thecomputer-implemented method of claim 31, wherein the patient specificdata comprises patient interaction data.
 35. The computer-implementedmethod of claim 31, wherein the revised patient care plan is presentedto a user on multiple electronic devices.
 36. The computer-implementedmethod of claim 31, wherein the revised patient care plan compriseselectronic content.
 37. The computer-implemented method of claim 31,further comprising determining the speed of the patient's electronicdevice and presenting the revised patient care plan based on the speedof the patient's electronic device.
 38. The computer-implemented methodof claim 31, wherein electronic data type presented in the revisedpatient care is determined based on a processor speed of the electronicdevice and a speed of an electronic network to which the electronicdevice connects.
 39. The computer-implemented method of claim 31,wherein the patient specific data is received from a patient'selectronic device and comprises patient environmental data.
 40. Thecomputer-implemented method of claim 31, wherein the patientenvironmental data is received from optical and audio components of theelectronic device.
 41. An information processing device for revising apatient care plan, the device comprising: a processor for processing aset of instructions; and a computer-readable storage medium for storingthe set of instructions, wherein the instructions, when executed by theprocessor, perform a method comprising: electronically receiving patientfeedback relating to the patient care plan; decoding patient specificdata and learning one or more patterns that cause an event based on thepatient specific data and one or more pattern recognition algorithms;determining one or more patient beneficial actions based on the learnedpatterns; and automatically preparing and presenting a revised patientcare plan based on the patient specific data, the identified one or morepatient beneficial actions, and the patient feedback.
 42. Theinformation processing device of claim 41, wherein the patient specificdata comprises data received from a patient body sample sensor.
 43. Theinformation processing device of claim 41, wherein the patient specificdata is a blood glucose level.
 44. The information processing device ofclaim 41, wherein the patient specific data comprises patientinteraction data.
 45. The information processing device of claim 41,wherein the revised patient care plan is presented to a user on multipleelectronic devices.
 46. The information processing device of claim 41,wherein the revised patient care plan comprises electronic content. 47.A non-transitory computer-readable medium storing a set of instructionsthat, when executed by a processor, perform a method of providing atreatment recommendation, the method comprising: electronicallyreceiving patient feedback relating to a patient care plan; decodingpatient specific data and learning one or more patterns that cause anevent based on the patient specific data; transmitting an alert relatedto a non-beneficial patient action; determining one or more patientbeneficial actions based on the learned patterns; and; automaticallypreparing a revised patient care plan based on the patient specificdata, the identified one or more patient beneficial actions, and thepatient feedback.
 48. The computer-readable medium of claim 47, whereinthe patient specific data is received from a patient's electronic deviceand comprises patient environmental data.
 49. The computer-readablemedium of claim 47, wherein the patient specific data comprises datareceived from a patient body sample sensor.
 50. The computer-readablemedium of claim 47, wherein the patient specific data is a blood glucoselevel.